10 August 2019

Fraud

Definition

Fraud = scientifc misconduct.

  • Falsifying or fabricating data.
  • This is intentional, not accidental.
  • Puts all science under a bad light.
  • Markedly different from QRPs (next).

Notable examples

Questionable research practices

QRPs

Coin termed by John, Loewenstein, & Prelec (2012).
See also Simmons, Nelson, & Simonsohn (2011).

  • Not necessarily fraud.
  • Includes the (ab)use of actually acceptable research practices.
  • Problem with QRPs:
    • Introduce bias (typically, in favor of the researcher’s intentions…).
    • Inflated power at the cost of inflated Type I error probability (\(\gg 5\%\)).
    • Results not replicable.

QRPs

Some examples (John et al., 2012; Schimmack, 2015):

  • Omit some DVs.
  • Omit some conditions.
  • Peeking: Sequential testing — Look and decide:
    • \(p > .05\): Collect more.
    • \(p < .05\): Stop.
  • Only report \(p<.05\) results.
  • \(p\)-hacking: E.g.,
    • exclusion of outliers dependent on \(p\).
    • \(p = .054 \longrightarrow p = .05\).
  • HARKing (Kerr, 1998): Convert exploratory result into research question.

Researcher’s degrees of freedom

  • Researchers have a multitude of decisions to make (experiment design, data collection, analyses performed); Wicherts et al. (2016).
  • It is very possible to manipulate results in favor of one’s interests.
  • This is now known as researcher’s degrees of freedom (Simmons et al., 2011).
  • Consequence: Inflated false positive findings (Ioannidis, 2005).

Fried (2017)

  • The 7 most common depression scales contain 52 symptoms.
  • That’s 7 different sum scores.
  • Yet, all are interpreted as `level of depression’.

A now famous example…

Prof. Brian Wansink at Cornell University.

His description of the efforts of a visiting Ph.D student:

I gave her a data set of a self-funded, failed study which had null results (…). I said, “This cost us a lot of time and our own money to collect. There’s got to be something here we can salvage because it’s a cool (rich & unique) data set.” I had three ideas for potential Plan B, C, & D directions (since Plan A had failed). I told her what the analyses should be and what the tables should look like. I then asked her if she wanted to do them.

Every day she came back with puzzling new results, and every day we would scratch our heads, ask “Why,” and come up with another way to reanalyze the data with yet another set of plausible hypotheses. Eventually we started discovering solutions that held up regardless of how we pressure-tested them. I outlined the first paper, and she wrote it up (…). This happened with a second paper, and then a third paper (which was one that was based on her own discovery while digging through the data).



This isn’t creative, thinking outside the box, or worthy in any way.

This is QRPing.

What happened to Wansink?

  • He was severely criticized, his work was scrutinized (e.g., van der Zee, Anaya, & Brown, 2017).
  • Over 100 (!!) errors in a set of four papers…
  • Has now 40 (!!) publications retracted (as of July 2019).
  • After a year-long internal investigation, he was forced to resign.

Other famous failures

Is it really that bad?…

Yes.

  • Martinson, Anderson, & Vries (2005): “Scientists behaving badly”.
  • Fanelli (2009): Meta-analysis shows evidence of science misconduct.
  • John et al. (2012): Evidence for QRPs in psychology.
  • Mobley, Linder, Braeuer, Ellis, & Zwelling (2013): Reported evidence of pressure to find significant results.
  • Agnoli, Wicherts, Veldkamp, Albiero, & Cubelli (2017): Evidence of QRPs, now in Italy.
  • Fraser, Parker, Nakagawa, Barnett, & Fidler (2018): In other fields of science.

Interestingly, science misconduct has been a longtime concern (see Babbage, 1830).

There are also some voices against this state of affairs (e.g., Fiedler & Schwarz, 2016).

But why?…

Why risking scientific misconduct?

It is strongly related to incentives (Nosek, Spies, & Motyl, 2012; F. Schönbrodt, 2015a).

  • “Publish or perish”:
    Publish a lot, at highly prestigious journals.
  • Journals only publish a fraction of all manuscripts.

(I)reproducibility

Threats to reproducible science



(Munafò et al., 2017)

Lack of replications

Until very recently (Makel, Plucker, & Hegarty, 2012).

Didn’t we see this coming?

Meehl (1967)

How poorly we build theory (see Gelman):

“It is not unusual that (e) this ad hoc challenging of auxiliary hypotheses is repeated in the course of a series of related experiments, in which the auxiliary hypothesis involved in Experiment 1 (…) becomes the focus of interest in Experiment 2, which in turn utilizes further plausible but easily challenged auxiliary hypotheses, and so forth. In this fashion a zealous and clever investigator can slowly wend his way through (…) a long series of related experiments (…) without ever once refuting or corroborating so much as a single strand of the network.”

Say what?…

Cohen (1962)

Low-powered experiments:

“(…) It was found that the average power (probability of rejecting false null hypotheses) over the 70 research studies was .18 for small effects, .48 for medium effects, and .83 for large effects. These values are deemed to be far too small.”

“(…) it is recommended that investigators use larger sample sizes than they customarily do.”

Timeline Gelman

Large-scale replication projects

Many Labs (Klein et al., 2014)

Replicability of 13 classic and contemporary effects across 36 independent samples totaling 6,344 participants.

See also Many Labs 2 (Klein et al., 2018), Many Labs 3 (Ebersole et al., 2016).

Open Science Collaboration (OSC, 2015)



A gazilion authors.

Publication policies

Psychological Science (Eich, 2014)

Basic and Applied Social Psychology

“The Basic and Applied Social Psychology (BASP) (…) emphasized that the null hypothesis significance testing procedure (NHSTP) is invalid (…). From now on, BASP is banning the NHSTP.”

Child Adolescent Mental Health (Spreckelsen, 2018)

The New England Journal of Medicin

Editorial (Harrington et al., 2019).

“(…) a requirement to replace P values with estimates of effects or association and 95% confidence intervals”

Education

Frank & Saxe (2012)

Sarafoglou, Hoogeveen, Matzke, & Wagenmakers (2019)

Research Master course on open science practices. Materials available!

Chambers (2017b)

Kiers, Hoekstra, Tendeiro, & Van Ravenzwaaij (2019)

\(p\)-values

Definition

Probability of an effect at least as extreme as the one we observed, given that \(\mathcal{H}_0\) is true.

\[\fbox{$ p\text{-value} = P\left(X_\text{obs} \text{ or more extreme}|\mathcal{H}_0\right) $}\]

The definition is simple enough, right?…

Test yourself

Consider the following statement (Falk & Greenbaum, 1995; Gigerenzer, Krauss, & Vitouch, 2004; Haller & Kraus, 2002; Oakes, 1986):

Suppose you have a treatment that you suspect may alter performance on a certain task. You compare the means of your control and experimental groups (say, 20 subjects in each sample). Furthermore, suppose you use a simple independent means \(t\)-test and your result is significant (\(t = 2.7\), \(df = 18\), \(p = .01\)). Please mark each of the statements below as “true” or “false.” False means that the statement does not follow logically from the above premises. Also note that several or none of the statements may be correct.

Test yourself

Results

All statements are incorrect. But how did students and teachers perceive these statements?

This was in 2004. But things did not improve since…

Goodman (2008)


Greenland et al. (2016)




This paper expands Goodman (2008) and elaborates on 25 (yes, 二十五!!!) misinterpretations.

If \(p\)-values are inflated… What to do?

Publication bias and QRPs (\(p\)-hacking) inflate \(p\)-values. Can we “deflate” them?

  • \(p\)-curve; see Simonsohn et al. (2014a), Simonsohn et al. (2014b), or a 5 min Youtube clip.

“\(p\)-curve is the distribution of statistically significant \(p\) values for a set of studies (\(ps < .05\)).”

  • \(z\)-curve; see

See F. Schönbrodt (2015b) for a nice presentation.

Confidence intervals

Definition

What do statistical associations advice?

ASA 2016 (Wasserstein & Lazar, 2016)

Six principles:

  1. \(p\)-values can indicate how incompatible the data are with a specified statistical model.
  2. \(p\)-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone.
  3. Scientific conclusions and business or policy decisions should not be based only on whether a \(p\)-value passes a specific threshold.
  4. Proper inference requires full reporting and transparency.
  5. A \(p\)-value, or statistical significance, does not measure the size of an effect or the importance of a result.
  6. By itself, a \(p\)-value does not provide a good measure of evidence regarding a model or hypothesis.

ASA 2019 (Wasserstein, Schirm, & Lazar, 2019)

This is an editorial of a special issue consisting of 43 (!!) papers.

Main ideas:

  • “Don’t” is not enough – Some what to do advices are provided.
  • However… Don’t say “statistically significant” – Just don’t.

“(…) it is time to stop using the term “statistically significant” entirely. Nor should variants such as “significantly different,” “\(p < 0.05\),” and “nonsignificant” survive, whether expressed in words, by asterisks in a table, or in some other way."

But:

“Despite the limitations of p-values (…), however, we are not recommending that the calculation and use of continuous \(p\)-values be discontinued. Where \(p\)-values are used, they should be reported as continuous quantities (e.g., \(p = 0.08\)). They should also be described in language stating what the value means in the scientific context.”

  • There is no unique “do”:

“What you will NOT find in this issue is one solution that majestically replaces the outsized role that statistical significance has come to play.”

  • Accept uncertainty. Be thoughtful, open, and modest.

  • Editorial, educational and other institutional practices will have to change.
    This includes: Journals, funding agencies, education, career system.

  • Value replicability, open materials and data, and reliable practices (which all take time) over “publish or perish”.

ASA 2019: Also advocate Bayesian statistics

What do experts advice?

Munafò et al. (2017)

Methods:

  • Protecting against cognitive biases
  • Improving methodological training
  • Implementing independent methodological support
  • Encouraging collaboration and team science

Munafò et al. (2017)

Key ideas

Better education (Button, 2018)

Registered reports

Visit the Center for Open Science.


Prior to data collection (Chambers, 2013):

  • Decide hypotheses, methods, and analysis.
    (Eliminate several QRPs, e.g., \(p\)-hacking, hiding null findings.)
  • Peer review of paper.
  • Conditional acceptance of paper!
  • Not only original studies, but also replications are of value!

Registered reports

As of July 2019, 205 journals use Registered Reports.

To learn:

  • Nosek & Lakens (2014): Special issue in Social Psychology in 2014, with examples.
  • Chambers, Feredoes, Muthukumaraswamy, & Etchells (2014): Includes useful FAQs.
  • Chambers (2017a): Slides at OSF.

Preregistration

Preregistration works (Kaplan & Irvin, 2015)

‘statcheck’

R package that can assist detecting statistical reporting of errors (Nuijten, Hartgerink, van Assen, Epskamp, & Wicherts, 2016).

What to avoid

Bullying

  • Debates in blogs, Twitter, and journals can be fierce.
  • Criticism should be part of science, of course.
  • It’s not bullying to criticize, of course, in particular, with grounded reasons (vide Wansink).
  • But sometimes criticism gets too carried away, IMHO.

NYT, 2017

(Interestingly: A comeback in Psychological Science.)

References

Agnoli, F., Wicherts, J. M., Veldkamp, C. L. S., Albiero, P., & Cubelli, R. (2017). Questionable research practices among italian research psychologists. PLOS ONE, 12(3), e0172792. doi: 10.1371/journal.pone.0172792

Babbage, C. (1830). Reflections on the Decline of Science in England: And on Some of Its Causes. http://www.gutenberg.org/files/1216/1216-h/1216-h.htm.

Button, K. (2018). Reboot undergraduate courses for reproducibility. Nature, 561, 287. doi: 10.1038/d41586-018-06692-8

Chambers, C. (2013). Registered reports: A new publishing initiative at Cortex. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior, 49(3), 609–610. doi: 10.1016/j.cortex.2012.12.016

Chambers, C. (2017a). Talks. doi: None

Chambers, C. (2017b). The seven deadly sins of psychology: A manifesto for reforming the culture of scientific practice. doi: 10.1515/9781400884940

Chambers, C., Feredoes, E., Muthukumaraswamy, S. D., & Etchells, P. (2014). Instead of "playing the game" it is time to change the rules: Registered Reports at AIMS Neuroscience and beyond. AIMS Neuroscience, 1, 4–17.

Cohen, J. (1962). The statistical power of abnormal-social psychological research: A review. The Journal of Abnormal and Social Psychology, 65(3), 145–153. doi: 10.1037/h0045186

Cuddy, A. J. C., Schultz, S. J., & Fosse, N. E. (2018). P-Curving a More Comprehensive Body of Research on Postural Feedback Reveals Clear Evidential Value for Power-Posing Effects: Reply to Simmons and Simonsohn (2017) - Amy J. C. Cuddy, S. Jack Schultz, Nathan E. Fosse, 2018. Psychological Science. doi: 10.1177/0956797617746749

Ebersole, C. R., Atherton, O. E., Belanger, A. L., Skulborstad, H. M., Allen, J. M., Banks, J. B., … Nosek, B. A. (2016). Many Labs 3: Evaluating participant pool quality across the academic semester via replication. Journal of Experimental Social Psychology, 67, 68–82. doi: 10.1016/j.jesp.2015.10.012

Eich, E. (2014). Business Not as Usual. Psychological Science, 25(1), 3–6. doi: 10.1177/0956797613512465

Falk, R., & Greenbaum, C. (1995). Significance Tests Die Hard - the Amazing Persistence of a Probabilistic Misconception. Theory & Psychology, 5(1), 75–98. doi: 10.1177/0959354395051004

Fanelli, D. (2009). How Many Scientists Fabricate and Falsify Research? A Systematic Review and Meta-Analysis of Survey Data. PLOS ONE, 4(5), e5738. doi: 10.1371/journal.pone.0005738

Fiedler, K., & Schwarz, N. (2016). Questionable Research Practices Revisited. Social Psychological and Personality Science, 7(1), 45–52. doi: 10.1177/1948550615612150

Flore, P. C., Mulder, J., & Wicherts, J. M. (2019). The influence of gender stereotype threat on mathematics test scores of Dutch high school students: A registered report. Comprehensive Results in Social Psychology, 1–35. doi: 10.1080/23743603.2018.1559647

Frank, M. C., & Saxe, R. (2012). Teaching Replication. Perspectives on Psychological Science, 7(6), 600–604. doi: 10.1177/1745691612460686

Fraser, H., Parker, T., Nakagawa, S., Barnett, A., & Fidler, F. (2018). Questionable research practices in ecology and evolution. PLOS ONE, 13(7), e0200303. doi: 10.1371/journal.pone.0200303

Fried, E. I. (2017). The 52 symptoms of major depression: Lack of content overlap among seven common depression scales. Journal of Affective Disorders, 208, 191–197. doi: 10.1016/j.jad.2016.10.019

Friese, M., Loschelder, D. D., Gieseler, K., Frankenbach, J., & Inzlicht, M. (2019). Is Ego Depletion Real? An Analysis of Arguments. Personality and Social Psychology Review, 23(2), 107–131. doi: 10.1177/1088868318762183

Gendron, M., Crivelli, C., & Barrett, L. F. (2018). Universality Reconsidered: Diversity in Making Meaning of Facial Expressions. Current Directions in Psychological Science, 27(4), 211–219. doi: 10.1177/0963721417746794

Gigerenzer, G., Krauss, S., & Vitouch, O. (2004). The null ritual : What you always wanted to know about significance testing but were afraid to ask. Sage.

Goodman, S. (2008). A dirty dozen: Twelve p-value misconceptions. Seminars in Hematology, 45(3), 135–140. doi: 10.1053/j.seminhematol.2008.04.003

Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Statistical tests, P values, confidence intervals, and power: A guide to misinterpretations. European Journal of Epidemiology, 31(4), 337–350. doi: 10.1007/s10654-016-0149-3

Hagger, M. S., Chatzisarantis, N. L. D., Alberts, H., Anggono, C. O., Batailler, C., Birt, A. R., … Zwienenberg, M. (2016). A Multilab Preregistered Replication of the Ego-Depletion Effect. Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 11(4), 546–573. doi: 10.1177/1745691616652873

Haller, H., & Kraus, S. (2002). Misinterpretations of significance: A problem students share with their teachers? Methods of Psychological Research, 7(1), 1–20.

Harrington, D., D’Agostino, R. B., Gatsonis, C., Hogan, J. W., Hunter, D. J., Normand, S.-L. T., … Hamel, M. B. (2019). New Guidelines for Statistical Reporting in the Journal. New England Journal of Medicine, 381(3), 285–286. doi: 10.1056/NEJMe1906559

Heathers, J. (2018). Alright, let’s have a roll-call of the big psychology studied that ate their own teeth for one reason or another. SOCIAL PRIMING. Lots of failed repos.http://www.slate.com/articles/health_and_science/science/2014/07/replication_controversy_in_psychology_bullying_file_drawer_effect_blog_posts.html … [Tweet]. https://twitter.com/jamesheathers/status/1006287906087071748.

Ioannidis, J. P. A. (2005). Why Most Published Research Findings Are False. PLOS Medicine, 2(8), e124. doi: 10.1371/journal.pmed.0020124

John, L. K., Loewenstein, G., & Prelec, D. (2012). Measuring the Prevalence of Questionable Research Practices With Incentives for Truth Telling. Psychological Science, 23(5), 524–532. doi: 10.1177/0956797611430953

Kaplan, R. M., & Irvin, V. L. (2015). Likelihood of Null Effects of Large NHLBI Clinical Trials Has Increased over Time. PloS One, 10(8), e0132382. doi: 10.1371/journal.pone.0132382

Kerr, N. L. (1998). HARKing: Hypothesizing After the Results are Known. Personality and Social Psychology Review, 2(3), 196–217. doi: 10.1207/s15327957pspr0203_4

Kiers, H., Hoekstra, R., Tendeiro, J., & Van Ravenzwaaij, D. (2019). Unconf - Implications of teaching Bayesian statistics to undergraduate psychology students.

Klein, R. A., Ratliff, K. A., Vianello, M., Adams, R. B., Bahník, Š., Bernstein, M. J., … Nosek, B. A. (2014). Investigating Variation in Replicability. Social Psychology, 45(3), 142–152. doi: 10.1027/1864-9335/a000178

Klein, R. A., Vianello, M., Hasselman, F., Adams, B. G., Adams, R. B., Alper, S., … Nosek, B. A. (2018). Many Labs 2: Investigating Variation in Replicability Across Samples and Settings. Advances in Methods and Practices in Psychological Science, 1(4), 443–490. doi: 10.1177/2515245918810225

Maes, E., Boddez, Y., Alfei, J. M., Krypotos, A.-M., D’Hooge, R., De Houwer, J., & Beckers, T. (2016). The elusive nature of the blocking effect: 15 failures to replicate. Journal of Experimental Psychology. General, 145(9), e49–71. doi: 10.1037/xge0000200

Makel, M. C., Plucker, J. A., & Hegarty, B. (2012). Replications in Psychology Research: How Often Do They Really Occur? Perspectives on Psychological Science, 7(6), 537–542. doi: 10.1177/1745691612460688

Martinson, B. C., Anderson, M. S., & Vries, R. de. (2005). Scientists behaving badly. Nature, 435(7043), 737. doi: 10.1038/435737a

Meehl, P. E. (1967). Theory-Testing in Psychology and Physics: A Methodological Paradox. Philosophy of Science, 34(2), 103–115.

Mobley, A., Linder, S. K., Braeuer, R., Ellis, L. M., & Zwelling, L. (2013). A Survey on Data Reproducibility in Cancer Research Provides Insights into Our Limited Ability to Translate Findings from the Laboratory to the Clinic. PLOS ONE, 8(5), e63221. doi: 10.1371/journal.pone.0063221

Munafò, M. R., Nosek, B. A., Bishop, D. V. M., Button, K. S., Chambers, C., Percie du Sert, N., … Ioannidis, J. P. A. (2017). A manifesto for reproducible science. Nature Human Behaviour, 1(1), 0021. doi: 10.1038/s41562-016-0021

Nosek, B. A., & Lakens, D. (2014). Registered reports: A method to increase the credibility of published results. Social Psychology, 45(3), 137–141. doi: 10.1027/1864-9335/a000192

Nosek, B. A., Spies, J. R., & Motyl, M. (2012). Scientific Utopia: II. Restructuring Incentives and Practices to Promote Truth Over Publishability. Perspectives on Psychological Science, 7(6), 615–631. doi: 10.1177/1745691612459058

Nuijten, M. B., Hartgerink, C. H. J., van Assen, M. A. L. M., Epskamp, S., & Wicherts, J. M. (2016). The prevalence of statistical reporting errors in psychology (19852013). Behavior Research Methods, 48(4), 1205–1226. doi: 10.3758/s13428-015-0664-2

Oakes, M. W. (1986). Statistical inference : A commentary for the social and behavioural sciences. Chichester: John Wiley & Sons.

Oostenbroek, J., Suddendorf, T., Nielsen, M., Redshaw, J., Kennedy-Costantini, S., Davis, J., … Slaughter, V. (2016). Comprehensive Longitudinal Study Challenges the Existence of Neonatal Imitation in Humans. Current Biology, 26(10), 1334–1338. doi: 10.1016/j.cub.2016.03.047

OSC. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716. doi: 10.1126/science.aac4716

Ranehill, E., Dreber, A., Johannesson, M., Leiberg, S., Sul, S., & Weber, R. A. (2015). Assessing the Robustness of Power Posing: No Effect on Hormones and Risk Tolerance in a Large Sample of Men and Women. Psychological Science, 26(5), 653–656. doi: 10.1177/0956797614553946

Reicher, S., & Haslam, S. A. (2006). Rethinking the psychology of tyranny: The BBC prison study. British Journal of Social Psychology, 45(1), 1–40. doi: 10.1348/014466605X48998

Ritchie, S. J., Wiseman, R., & French, C. C. (2012). Failing the Future: Three Unsuccessful Attempts to Replicate Bem’s “Retroactive Facilitation of Recall” Effect. PLoS ONE, 7(3). doi: 10.1371/journal.pone.0033423

Sarafoglou, A., Hoogeveen, S., Matzke, D., & Wagenmakers, E.-J. (2019). Teaching Good Research Practices: Protocol of a Research Master Course. Psychology Learning & Teaching, 1475725719858807. doi: 10.1177/1475725719858807

Schimmack, U. (2015). Questionable Research Practices: Definition, Detect, and Recommendations for Better Practices. https://replicationindex.com/2015/01/24/questionable-research-practices-definition-detect-and-recommendations-for-better-practices/.

Schönbrodt, F. (2015a). Questionable Research Practices. https://osf.io/bh7zv/.

Schönbrodt, F. (2015b). Red flags: How to detect publication bias and p-hacking. https://osf.io/cz7ht/.

Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant. Psychological Science, 22(11), 1359–1366. doi: 10.1177/0956797611417632

Simonsohn, U., Nelson, L. D., & Simmons, J. P. (2014a). P-curve: A key to the file-drawer. Journal of Experimental Psychology: General, 143(2), 534–547. doi: 10.1037/a0033242

Simonsohn, U., Nelson, L. D., & Simmons, J. P. (2014b). P-Curve and Effect Size: Correcting for Publication Bias Using Only Significant Results. Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 9(6), 666–681. doi: 10.1177/1745691614553988

Spreckelsen, T. F. (2018). Editorial: Changes in the field: Banning p-values (or not), transparency, and the opportunities of a renewed discussion on rigorous (quantitative) research. Child and Adolescent Mental Health, 23(2), 61–62. doi: 10.1111/camh.12277

Steele, K. M., Bass, K. E., & Crook, M. D. (1999). The Mystery of the Mozart Effect: Failure to Replicate. Psychological Science, 10(4), 366–369. doi: 10.1111/1467-9280.00169

Trafimow, D., & Marks, M. (2015). Editorial. Basic and Applied Social Psychology, 37(1), 1–2. doi: 10.1080/01973533.2015.1012991

Vadillo, M. A., Gold, N., & Osman, M. (2018). Searching for the bottom of the ego well: Failure to uncover ego depletion in Many Labs 3. Royal Society Open Science, 5(8), 180390. doi: 10.1098/rsos.180390

van der Zee, T., Anaya, J., & Brown, N. J. L. (2017). Statistical heartburn: An attempt to digest four pizza publications from the Cornell Food and Brand Lab. BMC Nutrition, 3(1), 54. doi: 10.1186/s40795-017-0167-x

Wagenmakers, E.-J., Beek, T., Dijkhoff, L., Gronau, Q. F., Acosta, A., Adams, R. B., … Zwaan, R. A. (2016). Registered Replication Report: Strack, Martin, & Stepper (1988). Perspectives on Psychological Science, 11(6), 917–928. doi: 10.1177/1745691616674458

Wasserstein, R. L., & Lazar, N. A. (2016). The ASA Statement on p-Values: Context, Process, and Purpose. The American Statistician, 70(2), 129–133. doi: 10.1080/00031305.2016.1154108

Wasserstein, R. L., Schirm, A. L., & Lazar, N. A. (2019). Moving to a World Beyond “p \(<\) 0.05”. The American Statistician, 73(sup1), 1–19. doi: 10.1080/00031305.2019.1583913

Watts, T. W., Duncan, G. J., & Quan, H. (2018). Revisiting the Marshmallow Test: A Conceptual Replication Investigating Links Between Early Delay of Gratification and Later Outcomes. Psychological Science, 29(7), 1159–1177. doi: 10.1177/0956797618761661

Wicherts, J. M., Veldkamp, C. L. S., Augusteijn, H. E. M., Bakker, M., van Aert, R. C. M., & van Assen, M. A. L. M. (2016). Degrees of Freedom in Planning, Running, Analyzing, and Reporting Psychological Studies: A Checklist to Avoid p-Hacking. Frontiers in Psychology, 7. doi: 10.3389/fpsyg.2016.01832